A standard problem in Retail Science is producing a week-by-week forecast of sales units for retail items. The sales of retail items in a given week is affected by many factors, such as seasonal factors, whether a discount has been applied to a retail item during the week, and at what point in the lifecycle of a merchandise the week falls. One common approach to forecasting weekly sales units involves building a “causal demand model” for retail items. This demand model is a mathematical model that describes weekly sales units in terms of factors such as the ones listed above. The factors are known as the “demand variables” for the demand model.
The demand model specifies mathematically how the demand variables affect sales units. For example, if the amount of discount is a demand variable, historical data may show that a 50% price cut resulted in a 4-fold increase in sales units. In this example, the demand variable is a 50% price cut and the historical sales data is the 4-fold increase in sales. In order for the causal demand model to be of use in forecasting sales units, it is necessary to determine the relationship of the demand variable (50% price cut) to the sales units (4-fold increase). This relationship is called the demand parameter associated with the demand variable.
In this example, the demand parameter may be determined to specify that for every 25% price reduction, sales of a particular retail item will increase by 2-fold. With the demand parameter determined, it is then possible to forecast sales units by specifying the future values of the demand variables. To continue the price-cut example, the retailer might know that next season, it will be running a 40% price cut during some weeks. The demand model will then forecast sales units for those weeks accounting for the 40% price cut.
The demand parameter is determined by examining historical retail sales data (known as retail panel data) containing price cuts for the retail item itself, or for similar retail items. However, as noted above, several demand variables affect the sales of retail items. These several demand variables apply simultaneously. For example, a retailer may have performed the 50% price cut during the summer for a summer item, in which case the 4-fold increase in sales may be partially due to an increase in seasonal demand for summer retail items during summer. To separate the effects of the several demand variables on sales, a regression is performed on the demand model to determine values for demand parameters that cause the demand model to best fit retail panel data.
Typically, thousands or hundreds of thousands of regressions are performed in a Retail Science application because a separate regression is performed for each segment of retail panel data. The usual way of segmenting sales data is by item and sales location. A typical retailer could have a very large number of combinations of retail items and locations. This type of segmenting is important to producing accurate sales forecasts, but greatly increases the amount of computation needed to produce the forecasts.